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Demonstrating Interoperable Channel State Feedback Compression with Machine Learning
Dani Korpi, Rachel Wang, Jerry Wang +20
Neural network-based compression and decompression of channel state feedback has been one of the most widely studied applications of machine learning (ML) in wireless networks. Var…
eess.SP2025
Beamforming with hybrid reconfigurable parasitic antenna arrays
Nitish Vikas Deshpande, Miguel Rodrigo Castellanos, Saeed R. Khosravirad +3
A parasitic reconfigurable antenna array is a low-power approach for beamforming using passive tunable elements. Prior work on reconfigurable antennas in communication theory is ba…